The aim of the mapspam2globiom R package is to facilitate the creation of country level crop distribution maps, which can be used as input by the IIASA’s Global Biosphere Management Model (GLOBIOM). GLOBIOM is a spatially explicit partial equilibrium model that is used to analyze the competition for land use between agriculture, forestry, and bioenergy. The model can be used for global and national level analysis (Valin et al. 2013; Leclère et al. 2014; Havlik et al. 2014). In the latter case, model output can be greatly improved by incorporating regionally specific information that is often provided by local stakeholders or can be taken from national statistics. Information on crop cover and the location of crops are a key driver of the model and it is therefore desirable to base these as much as possible on national sources of information.

mapspam2globiom includes a single function to aggregate crop distribution maps that were created with mapspamc package to the GLOBIOM input format. mapspamc (Dijk et al. 2023) was specifically developed to create national crop distribution maps using the Spatial Production Allocation Model (You and Wood 2006; You, Wood, and Wood-Sichra 2009; You et al. 2014; Yu et al. 2020). mapspam2globiom aggregates mapspamc output along two dimensions: (a) it aggregates raster cells to GLOBIOM simulation units (simu), which are clusters of grid cells with similar bio-physical characteristics and (b) it aggregates the 40+ mapspamc crops to the 18 crop groups in GLOBIOM.


To install mapspam2globiom:


Apart from the mapspam2globiom package, several other pieces of software are essential to run mapspam2globiom, which are described in the Installation section.


Dijk, Michiel van, Ulrike Wood-Sichra, Yating Ru, Zhe Guo, and Liangzhi You. 2023. “mapspamc: An R package to create crop distribution maps for country studies using a downscaling approach.” Preprint.
Havlik, Petr, Hugo Valin, Mario Herrero, Michael Obersteiner, Erwin Schmid, Mariana C Rufino, Aline Mosnier, et al. 2014. “Climate change mitigation through livestock system transitions.Proceedings of the National Academy of Sciences of the United States of America 111 (10): 3709–14.
Leclère, D, Petr Havlik, S Fuss, E Schmid, A Mosnier, B Walsh, H Valin, Mario Herrero, N Khabarov, and M Obersteiner. 2014. “Climate change induced transformations of agricultural systems: insights from a global model.” Environmental Research Letters 9 (12): 124018.
Valin, H, Petr Havlik, A Mosnier, Mario Herrero, E Schmid, and M Obersteiner. 2013. “Agricultural productivity and greenhouse gas emissions: Trade-offs or synergies between mitigation and food security?Environmental Research Letters 8 (3): 1–9.
You, Liangzhi, and Stanley Wood. 2006. “An entropy approach to spatial disaggregation of agricultural production.” Agricultural Systems 90 (1): 329–47.
You, Liangzhi, Stanley Wood, and Ulrike Wood-Sichra. 2009. “Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach.” Agricultural Systems 99 (2): 126–40.
You, Liangzhi, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2014. “Generating global crop distribution maps: From census to grid.” Agricultural Systems 127: 53–60.
Yu, Qiangyi, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang. 2020. “A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps.” Earth System Science Data 12 (4): 3545–72.